Abstract
The wind field environment surrounding long-span bridges is characterized by its complexity and variability, resulting in wind speed exhibiting random, nonlinear, and uncertain behavior. To enhance bridge safety and mitigate the impact of wind speed, it is crucial to establish a reliable wind speed prediction model. In this study, a structural health monitoring (SHM) system was deployed on a long-span bridge to collect extensive wind speed data, which was subsequently denoised using the wavelet decomposition (WD) method. Leveraging the long short-term memory (LSTM) approach, a wind speed prediction model (WD-LSTM) was developed. The study focuses on investigating the effects of three different thresholds (Bayesian threshold, SURE threshold, and Minmax threshold) in the WD method, the number of hidden units (2, 4, 8, 16, 32, 64, 128, 256, and 512) in the WD-LSTM model, and the number of inputs (one-step prediction, five-step prediction, ten-step prediction, and twenty-step prediction) in the WD-LSTM model on the prediction performance of wind speed. Evaluation metrics such as RMSE and R2 are employed for this analysis. Furthermore, the calculation time of the WD-LSTM prediction models with different hidden units and inputs is compared. Finally, an optimal WD-LSTM prediction model is proposed, taking into account both prediction accuracy and calculation time.
Key Words
long-span bridge; long short-term memory; structural health monitoring; wavelet decomposition; wind speed prediction
Address
(1) Yang Ding, Xue-Song Zhang:
State Key Laboratory of Mountain Bridge and Tunnel Engineering, Chongqing Jiaotong University, Chongqing 400074, China;
(2) Yang Ding, Jun Wang:
Department of Civil Engineering, Hangzhou City University, Hangzhou 310015, China;
(3) Ning-Yi Liang:
School of Civil Engineering, Changsha University of Science & Technology, Changsha 410114, China;
(4) Ning-Yi Liang:
Zhejiang Science Research Institute of Transportation, Hangzhou 310023, China;
(5) Chao-Qun Zeng:
School of Automobile and Transportation, Shenzhen Polytechnic University, Shenzhen, Guangdong 518055, China.
Abstract
As the members of temporary structures at construction sites are generally reused, inspection of individual temporary equipment members before use is essential to ensure safety during construction. Visual inspection, which is a common practice for assessing the quality of reused temporary equipment, is labor-intensive and depends on the skill of the inspector; thus, automated and accurate inspection methods are desired. However, little research has been devoted to the development of such methods. Because inspection criteria are mostly relevant to deformation, the shape measurement of temporary equipment members is the most fundamental consideration. This paper proposes an automated inspection method for temporary equipment based on measured 3D (three-dimension) shapes. A hardware system with a laser profiler and motion stage was designed to automatically measure 3D shapes. The 3D shape of each member has damage characteristics, such as hole deformation, thread abrasion, excessive clearance, and dents. Damage-detection algorithms for each inspection criterion were developed using machine learning and the geometric features of 3D shapes. The proposed method was validated using temporary equipment samples, with and without damage.
Amirhosein Parvizi, Vahidreza Amiresmaili, Seyyed Behnam Beheshti, Seyed Mohammad Ali Razavizadeh, Mojtaba Gorji Azandariani and Mohammad Reza Halvaeyfar
Abstract
Stepped spillway is one of the common types of spillways for water projects, and nowadays, it is considered due to its many benefits and they are used in many dam construction projects. The correct design and ensuring from proper these spillways require accurate analysis of the flow and has always been among the concerns of the dam's experts. Nowadays, using computers and numerical methods are a powerful tool for analyzing the flow and designing of water structures, and it is very useful in designing the primary spillways and optimizing them along with the construction of the hydraulic model. In this study, numerical modelling of flow over stepped spillways with different steps are presented, as well as optimization of the heights of steps. Also, a case study has been carried out about the Siah Bishe Dam spillway, in which the advantages and disadvantages of the numerical methods have been provided for optimizing the design. The computational fluid dynamics module of the finite element ANSYS FLUENT software was applied to assess the velocity vectors and pressure of the flow. According to the real flow is turbulent, the k–
Address
(1) Amirhosein Parvizi:
Department of Civil Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran;
(2) Vahidreza Amiresmaili:
Department of Civil Engineering, University of Saravan, Saravan, Iran;
(3) Seyyed Behnam Beheshti:
Department of Civil Engineering, Khomein Branch, Islamic Azad University, Khomein, Iran;
(4) Seyed Mohammad Ali Razavizadeh:
Department of Civil Engineering, National University of Skills (NUS), Tehran, Iran;
(5) Mojtaba Gorji Azandariani:
Structural Engineering Division, Faculty of Civil Engineering, Semnan University, Semnan, Iran;
(6) Mohammad Reza Halvaeyfar:
Department of Civil Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran.
Abstract
To enhance the accuracy of damage detection and prevent misjudgments when applying a single damage index, we have developed a method for multivariate data fusion damage detection based on signal denoising. This approach involves fusing two modal indicators and two vehicle excitation response indicators, ultimately performing a secondary fusion of the combined indicators. A statistical noise reduction method was applied to minimize noise in the fundamental indices. A modebased fusion index was created based on the curvature and displacement modes, whereas a fusion index based on the vehicle excitation response was generated by using the acceleration energy difference and acceleration energy-curvature difference. The Dempster-Shafer evidence theory was utilized for the secondary fusion of multiple fusion indices, leading to higher damage detection accuracy. The effectiveness of the damage identification method was confirmed by the ratio of the sub-peak value to the peak value. Moreover, numerical simulation data from a cable-stayed bridge further validated the damage detection method, showing a significant decrease in the ratio of the sub-peak value to the peak value (i.e., a reduction of 16-99%) after secondary fusion. These results demonstrate the feasibility of this method.
Abstract
Accurate pile-bearing capacity prediction is crucial for ensuring the stability and safety of deep foundations, particularly for tall buildings. This study investigates the use of four hybrid evolutionary computational models — Whale Optimization Algorithm (WOA), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), and Ant Lion Optimizer (ALO) — to enhance prediction accuracy. These models were evaluated for training and testing datasets based on their population sizes and performance metrics, such as the coefficient of determination (R2) and root mean square error (RMSE). The WOA model demonstrated the highest accuracy, achieving an R2 of 0.979 (training) and 0.968 (testing), along with RMSE values of 0.079 and 0.11, respectively. The ALO model followed closely, with an R2 of 0.989 (training) and 0.968 (testing), though it showed a higher RMSE in testing at 0.235. ABC and ACO, with R2 values ranging between 0.883 and 0.958, displayed lower accuracy than WOA and ALO. The models were ranked based on their performance, with WOA obtaining the highest total rank, followed by ALO, while ABC and ACO shared a similar total rank. These findings highlight the potential of hybrid evolutionary models for improving pile-bearing capacity predictions, which is vital for geotechnical engineering applications.
Key Words
driven piles; hybrid; nature-inspired; predicting; shaft friction capacity
Address
(1) Yanhua Zhang:
Department of Physics and Electronic Engineering, Yuncheng University, Yuncheng City, Shanxi Province, China;
(2) Mesut Gör:
Department of Civil Engineering, Division of Geotechnical Engineering, Firat University, 23119 Elâzığ, Turkey
(3) Hossein Moayedi:
Institute of Research and Development, Duy Tan University, Da Nang, Vietnam;
(4) Hossein Moayedi:
School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam;
(5) Yaghoub Zolfegharifar:
Department of Civil Engineering and Architecture, Islamic Azad University, Yasuj, Iran.
